141 results about "Density functional theory" patented technology

The invention belongs to the field of computational chemistry, and particularly relates to a multi-parameter collaborative dynamic screening method for adsorbed species on the surface of a catalyst. Comprising the following steps that an initial crystal structure is constructed and optimized through VESTA software, and a stable crystal structure model is obtained through density functional theory optimization; integrating working condition parameters such as a solvent, pH and potential, and drawing a Bayer diagram to label adsorption species and a mapping relation; and calculating a zero charge point and capacitance to obtain electrochemical parameters, and determining stable adsorption species under actual reaction through constant potential molecular dynamics simulation. According to the scheme, a deep structure-activity relationship between an active site structure and catalytic activity under a working condition is established through integration of all working condition parameters, labeling of adsorption species by a Boubye diagram, constant potential molecular dynamics simulation and the like; the problems that a systematic method combined with actual working condition parameters is lacked in prediction of the adsorbed substances on the surface of the catalyst, and real adsorbed species are difficult to accurately screen are solved, and accurate capture and efficient screening are achieved.

The invention discloses a sodium ion battery positive electrode material preparation optimization method, which comprises: calculating an electronic structure of a positive electrode material by using a density functional theory, and analyzing a reaction mechanism, conductivity and electrochemical reaction kinetics of the positive electrode material; simulating the stability of the positive electrode material in a preset environment by using molecular dynamics, predicting the phase change process of the positive electrode material under different process conditions, and optimizing preset parameters of the positive electrode material; performing preset factor evaluation on the electrochemical performance of the positive electrode material through the macroscopic simulation model; micro data of a preset method and molecular dynamics simulation are used as input and transmitted to battery performance simulation of the macroscopic simulation model, preset parameters of the positive electrode material are optimized through a multi-scale optimization algorithm, and the optimal material formula and process are output. According to the preparation optimization method of the sodium-ion battery positive electrode material, simulation results of different scales can be mutually coupled, and accurate preparation optimization of the sodium-ion battery positive electrode material is provided.

The invention discloses a method for exploring a heterogeneous catalytic reaction network based on a deep potential energy surface model, and the method comprises the steps: carrying out the accurate prediction of the free energy change of all possible element reactions on a catalyst through the combination of transfer learning and a density functional theory, and constructing a reaction network diagram based on a graph theory method; an improved Dijkstra method is used for searching a reaction network diagram to determine a reaction mechanism, and the selectivity of the catalyst is explored based on a Markov chain method. According to the technical scheme provided by the invention, when the CO2 reduction reaction mechanism on the monatomic alloy is explored, the prediction error MAE of the electron energy of each intermediate is only 0.16 eV, the calculated amount is reduced by 90%, the dominant path of the CO2 reduction reaction is visually displayed, and a new normal form is provided for complex mechanism exploration and catalyst screening.

The invention discloses a hydroxyl polycyclic aromatic hydrocarbon Raman spectrum detection method based on a dynamic weighted voting fusion network, relates to the technical field of hydroxyl polycyclic aromatic hydrocarbon detection, and provides an effective method which is rapid, accurate and free of chromatographic separation. Spectral information of OH-PAHs is obtained by using surface enhanced Raman spectroscopy SERS, and a Raman spectrum is obtained through density functional theory DFT calculation. A dynamic weighted voting fusion network DWVF-Net model is constructed, wherein the model comprises two core parts of cross-modal attention fusion (CMAF) and dynamic weighted voting (DWV). The CMAF dynamically weights key areas of SERS and DFT Raman spectrums through cosine similarity to realize feature fusion; and the DWV adaptively adjusts the weight of the model according to the real-time performance of the model, and optimizes an integration decision. Through the synergistic effect of CMAF and DWV, complementary information of SERS and DFT Raman spectrums is effectively fused, and the adaptability advantage of a dynamic weighting mechanism in the aspect of processing a complex data environment is verified.

The invention provides a performance prediction method for a hafnium oxide semiconductor material structure, and relates to the technical field of semiconductor material research and development. The method comprises the following steps: performing global search in an Hf-O binary system by using crystal structure prediction software, determining the most stable two-dimensional Hf2O4 crystal structure, and further confirming the crystal configuration through structure optimization; calculating an Hf2O4 crystal structure by using density functional theory calculation software, and evaluating the dynamic, thermodynamic and mechanical stability of Hf2O4 by using a calculation result system; the electronic property of the Hf2O4 is obtained through further deep calculation, and the structural stability and semiconductor characteristics of the Hf2O4 are verified. According to the invention, through the first principle and the evolutionary algorithm calculation, the simulation calculation is used to replace the traditional trial-and-error experiment, and the novel semiconductor material with high reliability can be efficiently explored; a theoretical basis is provided for the design of a novel high-performance semiconductor material in advanced chip manufacturing, the research and development period can be expected to be remarkably shortened, and the cost can be reduced.

The invention discloses a method and system for predicting stainless steel corrosion in different soil environments and a medium, and belongs to the technical field of metal corrosion prediction.The method comprises the following steps that multivariate environment data are collected; obtaining macroscopic corrosion characteristic parameters of the stainless steel in the soil simulation solution through electrochemical testing; acquiring microscopic corrosion characteristics of the stainless steel; calculating and obtaining stainless steel microscopic corrosion theoretical parameters based on a density functional theory; carrying out normalization and feature extraction on the feature parameters, and constructing a multi-dimensional feature vector; constructing a coupling prediction model of the multi-layer perceptron and support vector regression; and performing error reverse correction on the model based on actually measured data, and outputting a stainless steel corrosion rate predicted value. According to the method, a corrosion mechanism is disclosed from an atomic scale through density functional theory calculation, and a coupling prediction model of a multi-layer perceptron and support vector regression is constructed through multi-source data, so that the prediction precision is improved.

The invention relates to the technical field of pharmaceutical analysis, and discloses a method for monitoring a baicalein-betaine eutectic process based on terahertz spectroscopy, and the method comprises the following steps: mixing and ball-milling baicalein and betaine to obtain powder samples, collecting the powder samples with different ball-milling times, drying, mixing with a dispersing agent, tabletting, and drying to obtain the baicalein-betaine eutectic crystal. The method comprises the following steps: measuring spectral data by using terahertz time-domain spectroscopy equipment, selecting a characteristic peak and tracking intensity change to obtain a curve, carrying out nonlinear fitting on the curve, obtaining a molecular vibration mode through a density functional theory, and analyzing the eutectic process by combining the two modes. The terahertz spectrum technology is combined with theoretical calculation, complete analysis of the baicalein-betaine eutectic process from macrodynamics monitoring to microcosmic molecular action mechanism analysis is achieved, and an effective means is provided for eutectic formation research and process optimization.

The invention discloses a method and system for simulating the operating environment of a hydrogen energy reaction kettle, and the method comprises the steps: building a multi-scale simulation frame based on the hydrogen energy reaction kettle, and carrying out the modeling of a micro scale, a mesoscale and a macro scale of the hydrogen energy reaction kettle; performing multi-physical field coupling simulation on the hydrogen energy reaction kettle, including coupling calculation of a flow field, a temperature field, a concentration field and a stress field in an operation environment of the hydrogen energy reaction kettle; performing intelligent optimization simulation based on a multi-scale simulation framework and multi-physics field coupling simulation, wherein the intelligent optimization simulation comprises adaptive grid optimization, non-uniform inlet simulation and two-phase flow simulation; and constructing a digital twin platform, and carrying out system-level dynamic simulation and intelligent control on the hydrogen energy reaction kettle based on the digital twin platform. According to the method, all-around modeling and simulation from a micro scale to a macro scale are realized through a plurality of methods such as a density functional theory, molecular dynamics, a lattice Boltzmann method and computational fluid dynamics.

The invention discloses a prediction method for evaluating the surface silanization reaction efficiency of white carbon black, and the method comprises the steps: collecting the structural information of ionic liquid in an object to be evaluated, drawing the structural formula of the corresponding ionic liquid by using chemdraw software, then importing chem3D software, storing the structural formula as a gjf file, loading Gaussian 09 software, optimizing the minimum point based on the density functional theory, and calculating the surface silanization reaction efficiency of the object to be evaluated. A chk file containing minimum point wave function information is obtained, and a Gaussian 09 built-in formchk tool is used for converting the chk file into an fchk file; and loading the fchk file into a Multiwfn program, and carrying out wave function analysis on the obtained minimum point to obtain the molecular surface quantitative descriptor for evaluating the catalytic activity of the ionic liquid. The method is a method based on the density functional theory and molecular surface quantitative analysis, the obtained molecular surface quantitative descriptor has good correlation with the ILs catalyzed white carbon black and silane coupling agent reaction efficiency data set obtained by a model compound experiment, and the method has reliability and practicability.

An information processing device according to an embodiment is provided with at least one memory and at least one processor. The at least one processor: calculates, at each of a plurality of positions in a system to be calculated, a descriptor based on values obtained by convolving results obtained by performing a linear operation on respective eigenfunctions of a plurality of electrons in an equation used in a density functional theory; inputs at least the descriptor to a trained model at each of the plurality of positions and outputs the exchange correlation energy density of each of the plurality of electrons; applies the exchange correlation energy density to the equation at each of the plurality of positions and calculates the eigenfunction of each of the plurality of electrons and the eigenenergy of each of the plurality of electrons; and calculates the energy of the system by using the eigenfunctions and the eigenenergies.

The invention provides an evaluation method and evaluation equipment for the free adsorption proportion of deep coal rock gas, and relates to the technical field of coal rock gas development. The method comprises the following steps: constructing micro-pore composite models with different rock pore diameters based on a to-be-evaluated coal rock sample; wherein the micro-pore composite model is constructed based on the material composition of the to-be-evaluated coal rock sample and the full-scale pore characteristics of the coal rock sample; setting simulation environment parameters of the micro-pore composite model, adsorbing preset gas into the micro-pore composite model, enabling the micro-pore composite model to reach balance based on a density functional theory, and establishing a dynamic evolution model by changing the pore diameter of the micro-pore composite model to obtain simulation data under different simulation environment parameters; and based on the simulation data under different simulation environment parameters and a preset free adsorption gas proportion model, determining the deep coal rock gas free adsorption proportion. According to the method, the accuracy of the free adsorption proportion of the deep coal rock gas can be improved.

The invention discloses a simulation modeling method and device for a nanoscale device, relates to the technical field of semiconductor modeling and simulation, and aims to solve the problem that efficiency and precision cannot be considered in nanoscale device modeling and simulation in the prior art. The method comprises the steps that key material parameters are extracted from electronic structure basic data of a target nanoscale device, a default material parameter library in TCAD software is corrected based on the key material parameters, and a corrected material parameter library is obtained; electronic structure basic data is obtained based on a density functional theory; based on the corrected material parameter library, optimizing adjustable parameters of a ballistic transport model preset in TCAD software in combination with a transport IV curve to obtain a ballistic transport model after parameter optimization; the transportation IV curve is generated by adopting a quantum transportation method based on an unbalanced Green function in combination with key material parameters; and integrating the key material parameters with the ballistic transport model after parameter optimization, and constructing a simulation parameter library of the target nanoscale device.

The invention discloses an analysis method of an XLPE thermo-oxidative aging structure evolution and insulating property correlation mechanism based on microscopic parameters. The analysis method comprises the following steps: step 1, constructing a periodic structure model of an oxygen-containing cross-linked XLPE and carrying out geometric optimization; step 2, acquiring XLPE structure models under different thermo-oxidative aging degrees by utilizing de novo molecular dynamics; 3, on the basis of a density functional theory (DFT), calculating microcosmic parameters, related to insulation performance, of XLPE of different structures under different electric field intensities by utilizing Gaussian software; and 4, depicting a'thermal oxidation aging degree-microscopic parameter 'radar map based on the change rule of the microscopic parameters of the XLPE under different thermal oxidation aging degrees, and constructing a structure-function relationship between the microscopic parameters and the different thermal oxidation aging degrees. According to the method, the thermo-oxidative aging process of the XLPE is simulated on the basis of AIMD, differences of microcosmic parameters of the XLPE under different aging degrees are compared, and the structure-function relationship between the thermo-oxidative aging degrees and the microcosmic parameters of the cross-linked XLPE is constructed more accurately.

The invention discloses an environment-friendly mixed insulating gas liquefaction temperature prediction method based on mean square displacement MSD and conformational analysis. The method comprises the following steps that 1, a system model of environment-friendly insulating gas and a gas mixture of the environment-friendly insulating gas is established; step 2, utilizing Materials Studio simulation software to simulate the molecular dynamics process of the environment-friendly mixed insulating gas at different temperatures based on an NPT ensemble; 3, analyzing mean square displacement MSD of the environment-friendly mixed insulating gas based on a molecular dynamics simulation result, and calculating average mean square displacement; 4, analyzing the conformational change of the environment-friendly mixed insulating gas based on a molecular dynamics simulation result; and 5, predicting the liquefaction temperature of the environment-friendly mixed insulating gas by integrating the mean square displacement and the conformation analysis result. Based on the density functional theory, the liquefaction temperature of the environment-friendly mixed insulating gas is conveniently and quickly predicted through molecular dynamics simulation.

The invention relates to a characterization method of a thermal decomposition mechanism of an azo initiator. The characterization method comprises the following steps: (1) carrying out thermal stability analysis on the azo initiator; (2) acquiring microscopic decomposition characteristic data of the azo initiator by using a thermogravimetric-Fourier transform infrared spectroscopy combined technology; (3) constructing a three-dimensional molecular structure model of the azo initiator; (4) calculating the bond dissociation energy of the key covalent bond based on the density functional theory; and (5) the thermal decomposition characteristic temperature, the heat release behavior, the decomposition product and the evolution path of the azo initiator are determined by integrating the experimental data measured by the combination technology and the bond dissociation energy obtained by theoretical calculation, and the complete thermal decomposition mechanism of the azo initiator is clarified. According to the method, the thermal decomposition rule of the azo initiator is disclosed comprehensively and accurately through a technical path of combining experimental analysis and theoretical simulation and verifying macroscopic characteristics and micromechanism, and a key theoretical basis and technical support are provided for preventing and controlling the thermal runaway risk of such substances.

The invention relates to the technical field of information communication particularly suitable for specific application fields, and discloses a machine learning-based ionic liquid screening method for catalyzing CO2 cycloaddition reaction. The method comprises the following steps: constructing an ionic liquid three-dimensional structure database; calculating and generating a three-dimensional electrostatic potential and electron density data field through a density functional theory; voxelizing the data field into a standardized multi-channel three-dimensional tensor; utilizing a three-dimensional convolutional neural network regression model to automatically learn space electron characteristics and predict catalytic activity; and performing high-throughput virtual screening on the candidate library. Through end-to-end learning of the mapping relation between the three-dimensional electronic structure and the catalytic activity of the ionic liquid, the prediction accuracy and the screening efficiency are improved.

The invention discloses a method for predicting the lowest triplet excited state energy of receptor micromolecules, which comprises the following steps of: acquiring data and executing density functional theory (DFT) calculation to construct a database containing molecules and ET1 energy (namely descriptors) of construction fragments of the molecules; according to an ITIC series molecular modification strategy, statistical analysis is carried out on the structural features of all the construction units, and a redundant factor sigma exc is obtained; and predicting the T1 of the molecule through a machine learning algorithm by using the descriptor modified by the redundant factor sigmaexc. According to the method, the correction parameter of the redundant factor is provided and is applied to the rapid design and screening framework of the high-performance A-D-A OPV material of the ML algorithm. On the basis of the structure-function relationship, under modification of redundant factors, the photovoltaic characteristics of the molecules are coupled by means of constructing unit prediction molecules T1 and applying machine learning mathematics to quantify the data relationship, and the method has good interpretability. Through the trained ML model, direct, rapid and accurate ET1 prediction based on the molecular construction unit is realized.

The invention provides an iron-sulfur isotope equilibrium fractionation factor calculation method based on a density functional theory. The iron-sulfur isotope equilibrium fractionation factor calculation method comprises the following steps: firstly, constructing initial mineral models of amorphous FeS, cubic FeS, maquinol ore, hematite, hematite and pyrite; the model is optimized through a spin polarization DFT + U method, GGA-PBE functional, OTFG ultra-soft pseudo potential, 400 eV plane wave cutoff energy and 5.0 * 10 <-7 > Hartree self-consistent field convergence standards are selected as calculation parameters, and the optimal Hubbar Ueff value and magnetic sequence arrangement of each mineral are determined; and finally, calculating the simple harmonic vibration frequency of the mineral, and deducing equilibrium fractionation factors (1000ln beta value) of Fe and S isotopes at a specific temperature. According to the method, the theoretical blank of isotope fractionation of the conversion path from amorphous FeS to pyrite is filled up, the data can be used for judging the isotope balance of minerals and tracing the mineralization process, and an important theoretical basis is provided for research on sedimentary diagenesis and ancient ocean redox.

The invention discloses a high-performance biosensor based on a g-C3N4 / TiO2 heterojunction as well as a preparation method and application of the high-performance biosensor, and belongs to the technical field of biosensing and food safety detection. The sensor is constructed by taking a glassy carbon electrode as a substrate and sequentially modifying a g-C3N4 / TiO2 heterojunction composite material and diamine oxidase (DAO). The g-C3N4 / TiO2 heterojunction is prepared through a one-step calcination method, a type II p-n junction structure is formed, photo-generated charge separation and interface electron transfer are effectively promoted, and the conductivity, catalytic activity and enzyme immobilization capacity of the electrode are remarkably improved. The invention discloses a synergistic mechanism of heterojunction interface enhanced histamine selective adsorption and catalytic reaction through density functional theory calculation. The sensor shows excellent detection performance on histamine: the linearity is good in a range of 0.05-10 mM, and the detection limit is as low as 0.075 M; the selectivity is still higher than 93% in the presence of an interferent with ten times of concentration; when the method is applied to detection of actual samples of beef, mutton, fish and chicken, the adding standard recovery rate is 93.60%-101.8%, and the relative standard deviation is smaller than 5%. The sensor has the advantages of high sensitivity, good selectivity, strong anti-interference capability, good reproducibility and the like, and a reliable new method is provided for rapid and accurate screening of histamine in food.

The application discloses a calculation method of a reaction mechanism of cyanamide prepared in an alkaline environment and application thereof in byproduct control, and the calculation method comprises the following steps: step 1, constructing a molecular initial model of a reaction system containing a hydroxyl water molecular cluster, performing structural optimization and vibration analysis tasks on the model by using a density functional theory method, and calculating the molecular configuration to obtain a reasonable configuration without virtual frequency; step 2, searching for a reaction transition state based on the reasonable configuration of step 1, and verifying the reasonableness of the transition state; step 3, calculating the free energy in an alkaline aqueous solution environment, and drawing a potential energy surface diagram to determine the reaction rate control step; and step 4, selecting the reaction calculation hydrogen dissociation energy and hydrogen combination energy values of the reaction rate control step containing an intramolecular hydrogen migration process. The calculation method can simulate and calculate phenomena that are difficult to observe by experimental means, obtain reaction information of each elementary reaction process in an actual reaction, obtain the essential law of the reaction mechanism in the alkaline environment, and provide a theoretical reference for process improvement to reduce byproduct generation and promote the selectivity of the main reaction.

This invention provides a simulation method and application based on the slip ferroelectricity and piezoelectricity of two-dimensional heterolayer materials. The simulation method first constructs a g-C3N4 / graphene heterolayer model, forming various stacking configurations such as AA, AB, and BA. Then, based on density functional theory, structural optimization and electronic structure calculations are performed on the constructed heterolayer model. Based on this, the out-of-plane ferroelectric polarization and in-plane piezoelectric coefficient of the heterolayer under different stacking configurations are solved. Finally, combined with charge transfer characteristics and band structure analysis, the microscopic physical mechanism of the coexistence of slip ferroelectricity and piezoelectric properties of the system is elucidated. This invention can provide a theoretical basis for multifunctional two-dimensional materials through this simulation method, and has broad prospects for applications in memory, sensors, and actuators.

The invention discloses a method for constructing a stable distribution structure of oxygen-containing functional groups on the surface of graphene oxide, the stable distribution structure of the oxygen-containing functional groups on the surface of the graphene oxide can be constructed through density functional theory calculation on the basis of the microcosmic local distribution thermodynamic law of the oxygen-containing functional groups on the surface of the graphene oxide, the construction process is simple and efficient, and the method is suitable for industrial production. The dependence on computing resources is not high, and a scientific and reasonable theoretical structure model can be provided for related theoretical calculation research of the graphene oxide.

The invention provides a biphase composite magnetic material design calculation method and device, equipment and a medium, and relates to the technical field of magnetic materials. The method comprises the following steps: respectively carrying out structure optimization on single-phase unit cells of a soft magnetic phase and a hard magnetic phase to obtain a stable single-phase unit cell structure; combining a soft magnetic phase and a hard magnetic phase based on magnetic performance analysis of a single-phase unit cell structure; and in consideration of atomic bonding, lattice mismatch and orbital hybridization, calculation parameters are set to execute atomic relaxation, and the energy-stable combined unit cell model of the biphase composite magnetic material is obtained. On the basis of the density functional theory, the influence of the interfacial effect and different soft and hard magnetic phase thickness ratios on the magnetic performance in the double-phase composite magnetic material is researched, so that the screening and design process of a novel magnetic material meeting specific function requirements can be remarkably accelerated; and a powerful theoretical basis and a material basis are provided for developing next-generation energy-saving and reliable power electronic equipment.

The invention discloses a nondestructive testing method for structural characteristics and adsorption and permeability of paper, and belongs to the technical field of papermaking engineering. According to three-dimensional reconstruction, a density functional theory and computational fluid mechanics, the structural characteristics and adsorption and permeability of the paper are subjected to lossless characterization. The calculation tools can systematically explore the interaction mechanism of the deacidification agent and the paper on the molecular level and can integrally simulate the permeation process of fluid in the pore structure of the paper, a non-destructive prediction way is provided, and a theoretical basis and a new research method are provided for optimizing selection and formula design of the deacidification agent.

The invention provides an LET microscopic confirmation method and device, electronic equipment and a storage medium, and relates to the technical field of semiconductor data processing, and the method comprises the steps: setting initial conditions of a semiconductor material and charged particles; simulating the movement of the charged particles in the semiconductor material based on a time-dependent density functional theory to obtain a simulation result; and determining the LET according to the simulation result. The accurate setting of the initial condition provides a reliable input basis for the simulation process, and the TDDFT-based simulation process can truly restore the interaction mechanism of the charged particles and the semiconductor material from the microscopic level, thereby avoiding the defect of lack of electronic structure information in the calculation of the traditional empirical formula. And LET is determined through directional analysis of a simulation result, so that the accuracy and scientificity of LET calculation are remarkably improved, and powerful theoretical support is provided for engineering application such as radiation hardening design of semiconductor devices and research and development of radiation protection materials.

The application relates to the field of environmental science and technology, and discloses a fine-source analysis method for atmospheric pollutants combining computational chemistry and isotopes, which comprises the following steps: constructing an isotopic data set, simulating a target pollutant molecular configuration, constructing an isotopic fractionation paradigm equation, correcting local isotopic fractionation, tracing the generation mechanism of a target pollutant in a typical region, and optimizing source analysis of the target pollutant in the typical region. In this way, based on computational chemistry, the density functional theory and the U-B-GM model are coupled, the isotopic fractionation paradigm equation is constructed for a preset reaction path, the specific isotopic fractionation value or fractionation coefficient is accurately simulated in combination with atmospheric environmental parameters during observation, and a local isotopic fractionation correction scheme is established accordingly; the correction scheme is oriented towards fine-source analysis, and can accurately identify the generation mechanism and pollution sources. In this way, the uncertainty of source analysis can be effectively reduced, and fine tracing of the sources of atmospheric pollutants can be realized.

The invention relates to the technical field of thin film structure design, and particularly provides an optical parameter calculation method, a prediction model training method and a wavelength selection emitter design method. According to the optical parameter calculation method provided by the invention, the optical parameters of the target material at any preset temperature can be calculated based on crystal structure data of the target material, a density functional theory, a Lorentz phonon resonance model, a first principle molecular dynamics simulation method and a Boltzmann transport equation; an optical database of a material can be perfected, so that the design coverage of the wavelength selection emitter is more comprehensive. And on the basis of the Lorentz phonon resonance model, in the process of calculating the optical parameters of the target material, phonon spectrum infrared activity optical branch transverse and longitudinal wave splitting is considered, so that the accuracy of the calculated optical parameters of the infrared spectrum band can be improved, and the designed wavelength selection emitter is further optimized.

The invention discloses a nanometer material performance prediction method and system based on deep learning, and the method comprises the steps: obtaining the structure data of a nanometer material, inputting the structure data into a pre-trained structure performance prediction model, and obtaining the configuration space and performance prediction value of the nanometer material; sampling the configuration space by adopting a Monte Carlo method, and if the performance predicted value exceeds a preset threshold value, determining the configuration corresponding to the performance predicted value exceeding the preset threshold value as a candidate sampling point to obtain a candidate sampling point set; calculating an energy value of each sampling point in the candidate sampling point set by adopting a density functional theory, and constructing an energy landscape model according to the energy value of each sampling point; and obtaining energy change data from the energy landscape model, and inputting the energy change data into the optimized structural performance prediction model to obtain a phase change path and a dynamic characteristic prediction result of the nano material. The accuracy and efficiency of performance prediction of the nano material can be improved.

The invention provides a comprehensive molecular simulation optimization method for DMMP and derivatives thereof. The method comprises the following steps: S1, constructing an initial three-dimensional model of DMMP parent molecules and structural variants thereof; s2, performing conformation search on molecules of the component S1 to obtain a low-energy conformation; s3, aiming at the low-energy conformation in the S2, geometric structure optimization is carried out based on a density functional theory, and the stability of the optimized structure is verified through frequency calculation; s4, carrying out electronic structure analysis on the optimized structure passing the stability verification; s5, performing spectrum prediction on the optimized structure passing the stability verification; and S6, based on the results of S4 and S5, predicting the prospect of the structural variant as the flame retardant. According to the method, a reliable theoretical research idea is provided for researching a novel efficient flame retardant and designing a molecular structure by systematically researching the influence of the structural change on the physicochemical properties and flame-retardant efficiency of molecules.

The present invention provides a method for fabricating a novel self-powered photoelectrochemical photodetector with bias-driven PPC behavior, which utilizes the phenomenon of photocurrent polarity conversion (PPC). According to the characteristics of the s-type heterostructure, the Bi2O2S nanosheets are combined with the conductive polymer polyaniline (PANI) to form an organic-inorganic hybrid structure, successfully constructing a high-performance self-powered photoelectrochemical (PEC) photodetector with bias-driven PPC behavior. The electron transfer mechanism predicted by density functional theory (DFT) shows that the present invention conforms to the s-type heterostructure. Benefiting from this structure, the dark current density of the device is reduced by more than 20 times. In addition, the self-powered device exhibits PPC when tested at 0 V bias, and the negative photocurrent responsivity at 350 nm can reach -30 mA / W. This can already be comparable to commercially leading self-powered electronic products (the negative photocurrent responsivity at 365 nm is -31 mA / W). Overall, this study is significantly more versatile in performance than traditional detectors, has a wider application range, and has a simple manufacturing process and low cost, making it suitable for large-scale preparation, opening up a promising path for future research in the optoelectronic field.